The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Surgical data science for next-generation interventions
Open accessAbstract
Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.
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Predicting the future - big data, machine learning, and clinical medicine
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Birkmeyer, J. D., Finks, J. F., O'Reilly, A., Oerline, M., Carlin, A. M., Nunn, A. R., Dimick, J., Banerjee, M., & Birkmeyer, N. J. O. (2013) · New England Journal of Medicine · doi:10.1056/nejmsa1300625 · Cited by 1,504
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High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J., Adams, T., Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0304-0 · Cited by 1,967
Deep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406
Madani, A., Namazi, B., Altieri, M. S., Hashimoto, D. A., Rivera, A. M., Pucher, P. H., Navarrete-Welton, A., Sankaranarayanan, G., Brunt, L. M., Okrainec, A., et al. (2022) · Annals of Surgery · doi:10.1097/sla.0000000000004594 · Cited by 391
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315
Metadata
- Authors
- Maier-Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisenmann, M., Feussner, H., Forestier, G., Giannarou, S., Hashizume, M., Katic, D., Kenngott, H., Kranzfelder, M., Malpani, A., März, K., Neumuth, T., Padoy, N., Pugh, C., Schoch, N., Stoyanov, D., Taylor, R., Wagner, M., Hager, G. D., & Jannin, P.
- Venue
- Nature Biomedical Engineering
- Published
- 2017-09-12
- Volume
- 1
- Issue
- 9
- Pages
- 691-696
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W2580456502
- Cited by
- 566
- References
- 42
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- green
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- Maier-Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisenmann, M., Feussner, H., Forestier, G., Giannarou, S., Hashizume, M., Katic, D., Kenngott, H., Kranzfelder, M., Malpani, A., März, K., Neumuth, T., Padoy, N., Pugh, C., . . . Jannin, P. (2017). Surgical data science for next-generation interventions. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-017-0132-7
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
